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Abstract:

Functional magnetic resonance imaging (FMRI) is an imaging technique
for determining which regions of the brain are activated in response to a stimulus or
event. Early FMRI experiment paradigms were based upon those used in positron
emission tomography (PET), i.e. employing a block design consisting of extended
periods of `on? against `off ? activations. More recent experiments were based on
event-related FMRI, harnessing the fact that very short stimuli trains or single events
can generate robust responses. FMRI data suffer from low signal-to-noise ratios, and
typical event-related experiment paradigms employ selective averaging over many
trials before using statistical methods for determining active brain regions. The paper
reports a pattern recognition approach to the detection of single-trial FMRI responses
without recourse to averaging and at modest ?eld strengths (1.5 T). Linear discrimi-
nant analysis (LDA) was applied in conjunction with different feature extraction
techniques. Use of the unprocessed data samples as features resulted in single-
trial events being classi?ed with an accuracy of 61.0
? 9.5% over ?ve subjects. To
improve classi?cation accuracy, knowledge of the ideal template haemodynamic
response was used in the feature extraction stage. A novel application of parametric
modelling yielded an accuracy of 69.8
? 6.3%, and a matched ?ltering approach
yielded an accuracy of 71.9
? 5.4%. Single-trial detection of event-related FMRI may
yield new ways of examining the brain by facilitating new adaptive experiment
designs and enabling tight integration with other single-trial electrophysiological
methods.epi